Behavioral actions of alcohol: phenotypic relations from multivariate analysis of mutant mouse data

Behavioral studies on genetically diverse mice have proven powerful for determining relationships between phenotypes and have been widely used in alcohol research. Most of these studies rely on naturally occurring genetic polymorphisms among inbred strains and selected lines. Another approach is to introduce variation by engineering single-gene mutations in mice. We have tested 37 different mutant mice and their wild-type controls for a variety (31) of behaviors and have mined this data set by K-means clustering and analysis of correlations. We found a correlation between a stress-related response (activity in a novel environment) and alcohol consumption and preference for saccharin. We confirmed several relationships detected in earlier genetic studies, including positive correlation of alcohol consumption with saccharin consumption and negative correlations with conditioned taste aversion and alcohol withdrawal severity. Introduction of single-gene mutations either eliminated or greatly diminished these correlations. The three tests of alcohol consumption used (continuous two-bottle choice and two limited access tests: drinking in the dark and sustained high alcohol consumption) share a relationship with saccharin consumption, but differ from each other in their correlation networks. We suggest that alcohol consumption is controlled by multiple physiological systems where single-gene mutations can disrupt the networks of such systems.     

Genetic dissection of salicylic acid-mediated defense signaling networks in Arabidopsis

Properly coordinated defense signaling networks are critical for the fitness of plants. One hub of the defense networks is centered on salicylic acid (SA), which plays a key role in activating disease resistance in plants. However, while a number of genes are known to affect SA-mediated defense, relatively little is known about how these gene interact genetically with each other. Here we exploited the unique defense-sensitized Arabidopsis mutant accelerated cell death (acd) 6-1 to dissect functional relationships among key components in the SA hub. We show that while enhanced disease susceptibility (eds) 1-2 and phytoalexin deficient (pad) 4-1 suppressed acd6-1-conferred small size, cell death, and defense phenotypes, a combination of these two mutations did not incur additive suppression. This suggests that EDS1 and PAD4 act in the same signaling pathway. To further evaluate genetic interactions among SA regulators, we constructed 10 pairwise crosses in the acd6-1 background among mutants defective in: SA INDUCTION-DEFICIENT 2 for SA biosynthesis; AGD2-LIKE DEFENSE 1, EDS5, and PAD4 for SA accumulation; and NONEXPRESSOR OF PR GENES 1 for SA signaling. Systematic analysis of the triple mutants based on their suppression of acd6-1-conferred phenotypes revealed complex and interactive genetic relationships among the tested SA genes. Our results suggest a more comprehensive view of the gene networks governing SA function and provide a framework for further interrogation of the important roles of SA and possibly other signaling molecules in regulating plant disease resistance.     

The genetic architecture of behavioral canalization

Behaviors are components of fitness and contribute to adaptive evolution. Behaviors represent the interactions of an organism with its environment, yet innate behaviors display robustness in the face of environmental change, which we refer to as ‘behavioral canalization’. We hypothesize that positive selection of hub genes of genetic networks stabilizes the genetic architecture for innate behaviors by reducing variation in the expression of interconnected network genes. Robustness of these stabilized networks would be protected from deleterious mutations by purifying selection or suppressing epistasis. We propose that, together with newly emerging favorable mutations, epistatically suppressed mutations can generate a reservoir of cryptic genetic variation that could give rise to decanalization when genetic backgrounds or environmental conditions change to allow behavioral adaptation.     

Quantitative trait loci affecting starvation resistance in Drosophila melanogaster

The ability to withstand periods of scarce food resources is an important fitness trait. Starvation resistance is a quantitative trait controlled by multiple interacting genes and exhibits considerable genetic variation in natural populations. This genetic variation could be maintained in the face of strong selection due to a trade-off in resource allocation between reproductive activity and individual survival. Knowledge of the genes affecting starvation tolerance and the subset of genes that affect variation in starvation resistance in natural populations would enable us to evaluate this hypothesis from a quantitative genetic perspective. We screened 933 co-isogenic P-element insertion lines to identify candidate genes affecting starvation tolerance. A total of 383 P-element insertions induced highly significant and often sex-specific mutational variance in starvation resistance. We also used deficiency complementation mapping followed by complementation to mutations to identify 12 genes contributing to variation in starvation resistance between two wild-type strains. The genes we identified are involved in oogenesis, metabolism, and feeding behaviors, indicating a possible link to reproduction and survival. However, we also found genes with cell fate specification and cell proliferation phenotypes, which implies that resource allocation during development and at the cellular level may also influence the phenotypic response to starvation.     

Gene expression network reconstruction by convex feature selection when incorporating genetic perturbations

Cellular gene expression measurements contain regulatory information that can be used to discover novel network relationships. Here, we present a new algorithm for network reconstruction powered by the adaptive lasso, a theoretically and empirically well-behaved method for selecting the regulatory features of a network. Any algorithms designed for network discovery that make use of directed probabilistic graphs require perturbations, produced by either experiments or naturally occurring genetic variation, to successfully infer unique regulatory relationships from gene expression data. Our approach makes use of appropriately selected cis-expression Quantitative Trait Loci (cis-eQTL), which provide a sufficient set of independent perturbations for maximum network resolution. We compare the performance of our network reconstruction algorithm to four other approaches: the PC-algorithm, QTLnet, the QDG algorithm, and the NEO algorithm, all of which have been used to reconstruct directed networks among phenotypes leveraging QTL. We show that the adaptive lasso can outperform these algorithms for networks of ten genes and ten cis-eQTL, and is competitive with the QDG algorithm for networks with thirty genes and thirty cis-eQTL, with rich topologies and hundreds of samples. Using this novel approach, we identify unique sets of directed relationships in Saccharomyces cerevisiae when analyzing genome-wide gene expression data for an intercross between a wild strain and a lab strain. We recover novel putative network relationships between a tyrosine biosynthesis gene (TYR1), and genes involved in endocytosis (RCY1), the spindle checkpoint (BUB2), sulfonate catabolism (JLP1), and cell-cell communication (PRM7). Our algorithm provides a synthesis of feature selection methods and graphical model theory that has the potential to reveal new directed regulatory relationships from the analysis of population level genetic and gene expression data.     

Geometric interpretation of gene coexpression network analysis

THE MERGING OF NETWORK THEORY AND MICROARRAY DATA ANALYSIS TECHNIQUES HAS SPAWNED A NEW FIELD: gene coexpression network analysis. While network methods are increasingly used in biology, the network vocabulary of computational biologists tends to be far more limited than that of, say, social network theorists. Here we review and propose several potentially useful network concepts. We take advantage of the relationship between network theory and the field of microarray data analysis to clarify the meaning of and the relationship among network concepts in gene coexpression networks. Network theory offers a wealth of intuitive concepts for describing the pairwise relationships among genes, which are depicted in cluster trees and heat maps. Conversely, microarray data analysis techniques (singular value decomposition, tests of differential expression) can also be used to address difficult problems in network theory. We describe conditions when a close relationship exists between network analysis and microarray data analysis techniques, and provide a rough dictionary for translating between the two fields. Using the angular interpretation of correlations, we provide a geometric interpretation of network theoretic concepts and derive unexpected relationships among them. We use the singular value decomposition of module expression data to characterize approximately factorizable gene coexpression networks, i.e., adjacency matrices that factor into node specific contributions. High and low level views of coexpression networks allow us to study the relationships among modules and among module genes, respectively. We characterize coexpression networks where hub genes are significant with respect to a microarray sample trait and show that the network concept of intramodular connectivity can be interpreted as a fuzzy measure of module membership. We illustrate our results using human, mouse, and yeast microarray gene expression data. The unification of coexpression network methods with traditional data mining methods can inform the application and development of systems biologic methods.     

Genetics of alcohol consumption in Drosophila melanogaster

Individual variation in alcohol consumption in human populations is determined by genetic, environmental, social and cultural factors. In contrast to humans, genetic contributions to complex behavioral phenotypes can be readily dissected in Drosophila, where both the genetic background and environment can be controlled and behaviors quantified through simple high‐throughput assays. Here, we measured voluntary consumption of ethanol in ～3000 individuals of each sex from an advanced intercross population derived from 37 lines of the Drosophila melanogaster Genetic Reference Panel. Extreme quantitative trait loci mapping identified 385 differentially segregating allelic variants located in or near 291 genes at P < 10^?8. The effects of single nucleotide polymorphisms associated with voluntary ethanol consumption are sex‐specific, as found for other alcohol‐related phenotypes. To assess causality, we used RNA interference knockdown or P{MiET1} mutants and their corresponding controls and functionally validated 86% of candidate genes in at least one sex. We constructed a genetic network comprised of 23 genes along with a separate trio and a pair of connected genes. Gene ontology analyses showed enrichment of developmental genes, including development of the nervous system. Furthermore, a network of human orthologs showed enrichment for signal transduction processes, protein metabolism and developmental processes, including nervous system development. Our results show that the genetic architecture that underlies variation in voluntary ethanol consumption is sexually dimorphic and partially overlaps with genetic factors that control variation in feeding behavior and alcohol sensitivity. This integrative genetic architecture is rooted in evolutionarily conserved features that can be extrapolated to human genetic interaction networks.